Amazon Rainforest on Fire

Dot Map

Anyone can see a forest fire. Skill lies in sniffing the first smoke…
— Robert A. Heinlein

The Amazon Dashboard tracks individual fires in the Amazon region using a new approach to cluster and classify VIIRS active fire detections by fire type. Based on the fire location, intensity, duration, and spread rate, each individual event is classified as a deforestation fire, understory forest fire, small clearing & agricultural fire, or savanna fire.

Ingest

Amazon dashboard data

fire_type: (1) savanna and grassland, (2) small clearing and agriculture, (3) understory, and (4) deforestation fires
confidence: (1) low, (2) moderate, (3) high
frp Average: fire radiative power (FRP) for all fire detections within fire perimeter in megawatts (MW)
is_new: New fire start within past 24 hours (1) or existing fire (0)
is_active: Fire was active within the past 10 days (1) or not (0)

df <- read.csv("archetypes/amazon-rainforest-on-fire/fire-atlas-jan-july.csv", header = TRUE, stringsAsFactors = FALSE)
head(df, n=10)

Ingest

points for reference, cities

cities <- read.csv("archetypes/amazon-rainforest-on-fire/brazil-cities.csv", header = TRUE, stringsAsFactors = FALSE, fileEncoding = "utf-8")
head(cities, n=10)

Wrangle

reduce by filter

is new
is active
high confidence

df_new <- filter(df, is_new == 1)
df_new <- filter(df_new, is_active == 1)
df_new <- filter(df_new, confidence == 3)

# to use for manual (discrete) color scale
df_new <- df_new %>% mutate(fire_type = as.character(fire_type))

# head(df_new, n=10)

Base map

natural earth

ne_world <- ne_countries(scale = "small", returnclass = "sf")
# ne_world <- filter(ne_world, iso_a3 == "BRA")

Map layers

adm1, bosque protector, departamental, nacional, reserva florestal

adm1 <- st_read("archetypes/amazon-rainforest-on-fire/bra_admbnda_adm1_ibge_2020.geojson")

## Reading layer `bra_admbnda_adm1_ibge_2020' from data source 
##   `C:\Users\jaybe\Desktop\kamino-public\kamino-source\sources\10-maps\3-point\8-dot\archetypes\amazon-rainforest-on-fire\bra_admbnda_adm1_ibge_2020.geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 27 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -73.99047 ymin: -33.75077 xmax: -28.84917 ymax: 5.271131
## Geodetic CRS:  WGS 84

# bosque_protector <- st_read("archetypes/amazon-rainforest-on-fire/bosque-protector.geojson")
# departamental <- st_read("archetypes/amazon-rainforest-on-fire/departamental.geojson")
# nacional <- st_read("archetypes/amazon-rainforest-on-fire/nacional.geojson")
# reserva_florestal <- st_read("archetypes/amazon-rainforest-on-fire/reserva-florestal.geojson")

View

size by frp, color by fire_type

theme_opts <- theme(
  panel.background = element_rect(fill="#b9d2df", colour="white"),
  plot.background = element_blank(),
  legend.position = "none"
)

# to limit bounds of map view
xmin <- min(df_new$x)
xmax <- max(df_new$x)
ymin <- min(df_new$y)
ymax <- max(df_new$y)

fire_types <- c(
  "1" = "#77c3ff", # savanna and grassland
  "2" = "#e7ac03", # small clearing and agriculture
  "3" = "#6ba61b", # understory
  "4" = "#8d1900" # deforestation fires
)

v1 <- ggplot(data = ne_world) +
  geom_sf(fill="#f1efe9", color="#bbadbc", stroke=0.5) +
  geom_sf(data = adm1, fill=NA, color="#B2DFDB", stroke=0.5, alpha = 0.2 ) +
  # geom_sf(data = bosque_protector, fill="white", color="#B2DFDB", stroke=0.5, alpha = 0.2 ) +
  # geom_sf(data = departamental, fill="white", color="#B2DFDB", stroke=0.5, alpha = 0.2 ) +
  # geom_sf(data = nacional, fill="white", color="#B2DFDB", stroke=0.5, alpha = 0.2 ) +
  # geom_sf(data = reserva_florestal, fill="white", color="#B2DFDB", stroke=0.5, alpha = 0.2 ) +
  geom_point( data = df_new, aes(x = x, y = y, size = frp), shape = 21, color = "white", fill = "white", stroke = 3.0, alpha=0.4) +
  geom_point( data = df_new, aes(x = x, y = y, color = fire_type, fill = fire_type, size = frp), shape = 21, alpha=0.8) +
  geom_point( data = cities, aes(x = lng, y = lat), size = 2, color = "#333333", alpha=1.0) +
  scale_size_continuous(range=c(2,12)) +
  scale_color_manual ( values = fire_types ) +
  scale_fill_manual ( values = fire_types ) +
  coord_sf(xlim = c(xmin, xmax), ylim = c(ymin, ymax)) +
  labs(x = NULL,
       y = NULL,
       title = "Amazon Rainforest Fires", 
       subtitle="January to July, 2021") +
  theme_bw() +
  theme_opts

girafe(ggobj = v1, width_svg = 16, height_svg = 15,
       options = list(opts_sizing(rescale = TRUE, width = 0.65)))

References

citations for narrative and data sources

Data and Narrative: Global Fire Emissions Database, Amazon Dashboard, GO